Search Results for author:
Found 15 papers, 2 papers with code
URHand: Universal Relightable Hands
To simplify the personalization process while retaining photorealism, we build a powerful universal relightable prior based on neural relighting from multi-view images of hands captured in a light stage with hundreds of identities.
A Local Appearance Model for Volumetric Capture of Diverse Hairstyle
In this paper, we present a novel method for creating high-fidelity avatars with diverse hairstyles.
Elucidating STEM Concepts through Generative AI: A Multi-modal Exploration of Analogical Reasoning
Our study demonstrates the potential of applying large language models to educational practice on STEM subjects.
The Effectiveness of Large Language Models (ChatGPT and CodeBERT) for Security-Oriented Code Analysis
However, we observed that the strengths and limitations of adopting these LLMs to the code analysis have not been investigated.
Ranked #3 on
Code Generation
on MBPP
Leveraging Large Language Model and Story-Based Gamification in Intelligent Tutoring System to Scaffold Introductory Programming Courses: A Design-Based Research Study
Programming skills are rapidly becoming essential for many educational paths and career opportunities.
Which Features are Learned by CodeBert: An Empirical Study of the BERT-based Source Code Representation Learning
The Bidirectional Encoder Representations from Transformers (BERT) were proposed in the natural language process (NLP) and shows promising results.
NeuWigs: A Neural Dynamic Model for Volumetric Hair Capture and Animation
The capture and animation of human hair are two of the major challenges in the creation of realistic avatars for the virtual reality.
High-fidelity Face Tracking for AR/VR via Deep Lighting Adaptation
This paper addresses previous limitations by learning a deep learning lighting model, that in combination with a high-quality 3D face tracking algorithm, provides a method for subtle and robust facial motion transfer from a regular video to a 3D photo-realistic avatar.
Automated Segmentation of Brain Gray Matter Nuclei on Quantitative Susceptibility Mapping Using Deep Convolutional Neural Network
Abnormal iron accumulation in the brain subcortical nuclei has been reported to be correlated to various neurodegenerative diseases, which can be measured through the magnetic susceptibility from the quantitative susceptibility mapping (QSM).
Fully Convolutional Mesh Autoencoder using Efficient Spatially Varying Kernels
Learning latent representations of registered meshes is useful for many 3D tasks.
Automatic acute ischemic stroke lesion segmentation using semi-supervised learning
By using a large number of weakly labeled subjects and a small number of fully labeled subjects, our proposed method is able to accurately detect and segment the AIS lesions.
3D Guided Fine-Grained Face Manipulation
This is achieved by first fitting a 3D face model and then disentangling the face into a texture and a shape.
Towards Clinical Diagnosis: Automated Stroke Lesion Segmentation on Multimodal MR Image Using Convolutional Neural Network
The patient with ischemic stroke can benefit most from the earliest possible definitive diagnosis.
Hey, you, keep away from my device: remotely implanting a virus expeller to defeat Mirai on IoT devices
In particular, at a negotiated time slot, a customer is required to reboot the compromised device, then a "white" Mirai operated by the manufacturer breaks into the clean-state IoT devices immediately.
Cryptography and Security
Context-aware System Service Call-oriented Symbolic Execution of Android Framework with Application to Exploit Generation
We present novel ideas and techniques to resolve the challenges, and have built the first system for symbolic execution of Android Framework.
Cryptography and Security Software Engineering
